Differentiable Micro-Mesh Construction

Abstract

Micro-mesh (u-mesh) is a new graphics primitive for compact representation of extreme geometry consisting of a low-polygon base mesh enriched by per micro-vertex displacement. A new generation of GPUs supports this structure with hardware evolution on u-mesh ray tracing achieving real-time rendering in pixel level geometric details. In this article we present a differentiable framework to convert standard meshes into this efficient format offering a holistic scheme in contrast to the previous stage-based methods. In our construction context a u-mesh is defined where each base triangle is a parametric primitive which is then reparameterized with Laplacian operators for efficient geometry optimization. Our framework offers numerous advantages for high-quality u-mesh production: (i) end-to-end geometry optimization and displacement baking; (ii) enabling the differentiation of renderings with respect to umesh for faithful reprojectability; (iii) high scalability for integrating useful features for u-mesh production and rendering such as minimizing shell volume maintaining the isotropy of the base mesh and visual-guided adaptive level of detail. Extensive experiments on u-mesh construction for a large set of high-resolution meshes demonstrate the superior quality achieved by the proposed scheme.

Cite

Text

Dou et al. "Differentiable Micro-Mesh Construction." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00411

Markdown

[Dou et al. "Differentiable Micro-Mesh Construction." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/dou2024cvpr-differentiable/) doi:10.1109/CVPR52733.2024.00411

BibTeX

@inproceedings{dou2024cvpr-differentiable,
  title     = {{Differentiable Micro-Mesh Construction}},
  author    = {Dou, Yishun and Zheng, Zhong and Jin, Qiaoqiao and Shi, Rui and Li, Yuhan and Ni, Bingbing},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2024},
  pages     = {4294-4303},
  doi       = {10.1109/CVPR52733.2024.00411},
  url       = {https://mlanthology.org/cvpr/2024/dou2024cvpr-differentiable/}
}